[gov3009-l] Applied Statistics - 4/13 - Marie-Abele Bind

Aaron Kaufman aaronkaufman at fas.harvard.edu
Sun Apr 10 16:43:21 EDT 2016


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Marie-Abele
Bind*, Post-Doctoral Fellow at the Harvard University Center for the
Environment. She will be presenting work provocatively entitled *Valid and
Informative p-values from Big Data, Illustrated in an Epigenomic Cross-Over
Experiment**.*  Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/4132016-marie-abele-bind-harvard-title-coming-soon>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view
previous Applied Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: Valid and Informative p-values from Big Data, Illustrated in an
Epigenomic Cross-Over Experiment

Abstract: A common issue that arises with current analyses of epigenomic
data is the repeated use of statistical tests. For example, consider 17
people in a randomized experiment measuring the results of exposure to two
treatment conditions (e.g., clean air and ozone), with measurements at
484,531 epigenome locations, where the aim is to find the locations with an
epigenetic effect (i.e., of clean air versus ozone). Here, we describe the
use of randomization-based tests to obtain a Fisher exact p-value that is
valid whatever the correlational structure of the data from the epigenomic
locations. The power of the resultant test to detect real differences,
however, requires the careful a priori selection of the single test
statistic.

-- 
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
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